Customers' perceptions as an antecedent of satisfaction with online retailing services

Mwencha, Peter; Muathe, Stephen Article Customers' perceptions as an antecedent of satisfaction with online retailing services Journal of Marketing and Consumer Behaviour in Emerging Markets (JMCBEM) Provided in Cooperation with: Faculty of Management, University of Warsaw Suggested Citation: Mwencha, Peter; Muathe, Stephen (2018) : Customers' perceptions as an antecedent of satisfaction with online retailing services, Journal of Marketing and Consumer Behaviour in Emerging Markets (JMCBEM), ISSN 2449-6634, University of Warsaw, Faculty of Management, Warsaw, Iss. 1, pp. 4-27, https://doi.org/10.7172/2449-6634.jmcbem.2018.1.1 This Version is available at: https://hdl.handle.net/10419/311496 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/3.0/ © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 4 (4–27) Customers’ Perceptions as an Antecedent of Satisfaction with Online Retailing Services Peter Misiani Mwencha* (Corresponding Author) School of Business, Kenyatta University, P.O. Box 53555-00200 Nairobi, Kenya E-mail: [email protected] Stephen Makau Muathe School of Business, Kenyatta University, P.O. Box 43844 – 00100 Nairobi, Kenya E-mail: [email protected] Received: 29 November 2017/ Revised: 21 January 2018/ Accepted: 31 January 2018/Published online: 24 April 2018 ABSTRACT The assessment of antecedents of customer satisfaction has become very important for the success of online retailing services. This paper reports the results of a study that investigated the antecedent role of customers’ perceptions vis-a-vis satisfaction with online retailing services. While the study model conceptualizes customers’ perceptions as a composite variable made up of three dimensions (perceived attributes, perceived risk and perceived value) prescribed by four established information systems (IS) and consumer behaviour frameworks, namely the Technology Acceptance Model (TAM), Perceived Risk Theory (PRT), Theory of Consumption Values (TCV) and Expectations-Artifact Model of Satisfaction (EAMS), it does not specify how the different perceptual factors infl uence online satisfaction; instead it aggregates all three dimensions into a higher-order construct called “customers’ perceptions” and tries to understand the nature of relationship between the composite independent variable and the dependent variable. It employed a descriptive, correlational survey design whereby the response data collected from 240 registered users of 6 online retailers was analyzed using both descriptive as well as inferential statistics. The linear regression analyses indicate that the model provides a statistically signifi cant explanation of the variation in consumers’ online retailing satisfaction. The study also found empirical support for customers’ perceptions as an antecedent of satisfaction with online retailing services. JEL classifi cation: M00, M31 Keywords: online retailing, customer satisfaction, perceptions, e-commerce, online consumer behaviour 1. INTRODUCTION The commercial use of the internet has grown tremendously over the last two decades, characterized by a proliferation of various online-based electronic commerce (e-commerce) © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 5 (4–27) services. One of these services is online retailing (internet retailing, electronic retailing, or e-tailing), the direct sale from business to consumer (B-2-C) through electronic storefronts, typically designed around an electronic catalogue and shopping cart model (Stair & Rynolds, 2010). Due to its huge popularity, online retailing has had a signifi cant impact on several market segments such as travel, consumer electronics, hobby goods, and media goods across the globe (Weltevrenden & Boschma, 2008). Consequently, online retailing has developed to become an established marketing channel in its own right within the consumer marketplace (Doherty & Ellis-Chadwick, 2010). In the context of online shopping, the website of an online retailer is the main contact point by which the online retailer and consumers interface in the online shopping process (Ahn, Ryu & Han, 2004). Therefore, the quality of service rendered during the course of the whole online shopping transaction has signifi cant infl uence on customer satisfaction (Ho & Wu, 1999). While a high level of customer satisfaction does not necessarily guarantee customer loyalty, dissatisfaction will cause customers to take their business elsewhere. In other words, customers who are dissatisfi ed with the level of service they have received will be less likely to return in the future, or if they do return, they will most likely do so with less frequency than they did in the past. Conversely, customers who are extremely satisfi ed with their service experience with a given fi rm will most likely continue to return to that fi rm at the same frequency or even more frequently (Davis & Heineke, 1998). As a result, electronic satisfaction (e-satisfaction) has become vital for online retailers to attract and retain online shoppers in this virtual environment (Ting, Ariff, Zakuan, Sulaiman & Saman, 2016). With the ever increasing popularity of electronic commerce, the evaluation of antecedents and of customer satisfaction has become very important for the online shopping store vendors as well as for researchers (Ho & Wu, 1999). This is driven by the fact that customer satisfaction has become a hoped-for business outcome, and therefore focusing discussion on its antecedents is a necessary means to effect the desired outcome (Day & Crask, 2000). However, a review of past research literature suggests that there are various antecedent factors of customer satisfaction, since the researchers chose the variables and factors that best suit each circumstance in their perception (Jiradilok, Malisuwan, Madan, and Sivaraks, 2014). In the online context, a number of studies have shown that satisfaction is mainly affected by customers’ perceptions of their user experiences with information technology (IT) (Bhattacherjee, 2001a; 2001b). Therefore, in this study, satisfaction is conceptualized as an outcome of customers’ perceived performance of the e-retailing service. 2. LITERATURE REVIEW The literature review is drawn from past online consumer behaviour and information systems (IS) studies regarding two main variables: the independent variable – customer perceptions and the dependent variable – satisfaction. These variables are discussed in the following sections. The research hypothesis was developed based on theory from the combined literature. 2.1 Customer Satisfaction Customer satisfaction is often defi ned as the customers’ post-purchase comparison between pre-purchase expectation and performance received (Oliver, 1980; Zeithaml et al., 1993). Customer satisfaction is when products and services meet the expectation of the consumers (Kotler, Cunningham & Turner, 2001). Like traditional business, online fi rms also need to satisfy their customers. Normally, most satisfi ed customers intend to re-purchase the products if the product performance meets their expectation. Moreover, from a marketing perspective, satisfaction © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 6 (4–27) ensures that customers develop positive emotions towards brands, while dissatisfaction translates into negative brand emotions (Pizam et al., 2016). How one conceptualizes customer satisfaction also affects the modelling and measurement of the construct and its antecedents. Johnson et al. (1995) describe two basic conceptualizations of satisfaction, namely: transaction-specifi c and cumulative. Transaction-specifi c satisfaction is a customer’s transient evaluation of a particular product or service experience (Cronin & Taylor, 1992; Parasuraman et al., 1988). The cumulative model conceptualizes satisfaction as a cumulative construct that describes the total consumption experience with a product or service to date (Johnson and Fornell, 1991; Meeks, 1984; Van Raaij, 1981). Although transaction-specifi c satisfaction may provide insights into particular product or service encounters, cumulative satisfaction is arguably a better predictor of future behaviour (customer retention) and fi rm performance (profi tability). The approach employed in this study is both aggregate and cumulative, in line with Johnson, Nader & Fornell (1996). Accordingly, this study defi ned satisfaction as the customer’s overall positive evaluation of the online retailing service following initial usage or based on all prior interactions/encounters and shopping experiences. As demonstrated in past literature, customer satisfaction has been considered one of the most important concepts (McQuitty et al., 2000), and one of the main goals in marketing (Erevelles & Leavitt, 1992). Owing to the growing importance of online commerce, a number of studies have focused on online satisfaction (Nusair & Kandampully, 2008) because it helps to build customer trust (Flavian et al., 2006), enhances favourable word-of-mouth (Bhattacherjee, 2001), leads to repeat purchases (Kim, 2005), predicts purchase behaviour (McQuitty et al., 2000), projects the internet retailer’s endurance and success (Evanschitzky et al., 2004) and is critical for retaining current users (Bhattacherjee, 2001a; 2001b). It is therefore imperative to be able to understand and measure satisfaction in the context of e-commerce. According to Davis & Heineke (1998), defi ning customer satisfaction in service operations has been approached in two general ways: (1) satisfaction as a function of disconfi rmation; and (2) satisfaction as a function of perception. Owing to its complexity the disconfi rmation model has been criticized by some researchers (Teas, 1994; Goode & Moutinho, 1995) who prefer another approach. Consequently, the alternative approach that appears to be gaining acceptance is that satisfaction depends primarily on the customer’s perception of service performance rather than on the disconfi rmation between perception and expectation (Cronin & Taylor, 1994; Teas, 1993). This approach is in line with extant research evidence which demonstrates that overall customer satisfaction in a service encounter is infl uenced by customers’ perception (Brocato et al., 2012; Anderson et al., 2008; Sreejesh et al., 2017). If customer satisfaction is viewed as an outcome, then focusing discussion on its antecedents is necessary to effect the desired outcome (Day & Crask, 2000). This study will use this approach, whereby customers’ perceptions are conceptualized as having an antecedent effect on satisfaction with online retailing services. 2.2. Customers’ Perceptions Perceptions are essentially mental maps made by people to give them a meaningful picture of the world on which they can base their decisions (Berelson & Steiner, 1964). Perception occurs when stimuli are registered by one of the fi ve human senses: vision, hearing, taste, smell and touch (Hoyer & MacInni, 2008) via a process of sensing, selecting, and interpreting stimuli in the external, physical world into the internal, mental world (Wilkie, 1994). This perceptual process leads to a response which is either overt (actions) or covert (motivations, attitudes, and feelings) or both. From a consumer behaviour perspective, perceptions are an attempt by a consumer to obtain and process information about a market situation with a purpose to make himself aware of the © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 7 (4–27) market and market offerings (Sahaf, 2008). Consumers establish and continuously update their perceptions about the alternative products/services that they are considering and based on those perceptions, they determine their attitudes towards the products (preferences). According to Schiffman & Kanuk (2010), perception has strategy implications for marketers because consumers make decisions based on what they perceive rather than on the basis of objective reality. As a result, marketers have realized that understanding the perceptual process of consumers helps them to design better ways to help customers perceive their products and services favourably, especially since products and services that are perceived distinctly and favourably have a much better chance of being purchased than products or services with unclear or unfavourable images Consequently, both marketing and information systems (IS) researchers have over the years sought to establish how perceptions of an IT innovation infl uence satisfaction (Lin & Sun, 2009). It is therefore important that an online business understands the perceptions of the customer, as this can help the businesses to get a higher chance of satisfaction of their customers and at the same time attract and maintain their loyal customers (Yee & Yazdanifard, 2014). Consequently, for this study, the customers’ perceptions construct serves as the independent variable. It is composed of three constructs (perceived attributes, perceived risk and perceived value) identifi ed in extant service management, consumer behaviour and technology adoption literature as playing an antecedent role via-a-vis satisfaction with online retailing services. However, in this study, the three dimensions are aggregated to form a composite higher-level construct known as “customers’ perceptions” in order to investigate the relationship between the composite independent variable and the dependent variable. These variables are discussed in the following sections. 2.2.1. Perceived Attributes Perceived attributes (PA) refer to the perception towards the primary characteristics of innovations by actual adopters and potential adopters. The behaviour of individuals is predicated by how they perceive these primary attributes. Because individuals might perceive primary characteristics in different ways, their eventual behaviours might differ (Moore & Benbasat, 1991). PAs have been found to infl uence consumer usage patterns vis-à-vis information and communications technology (ICT), whereby users would perceive the attributes of these innovations favourably, while non-users and rejecters perceive them unfavourably enough not to use them (Rugimbana & Iversen, 1994). In this study, PA is an aggregate variable of three dimensions (perceived usefulness, perceived compatibility and perceived ease of use) drawn from the work of Davis’ TAM (1989), Rogers’ IDT (1995; 2003), and Moore and Benbasat’s PCI model (1991). 2.2.2. Perceived Risk Perceived risk (PR) is a subjective concept that relates to the uncertainty and consequences associated with a consumer’s action. A perception of risk with regard to purchasing or using a product or service dissuades a consumer from taking further action in that regard (Sharma, Durand & Gur-Arie, 1981; Bhatnagar, Misra & Rao, 2000). Due to the personal nature of such assessments, it cannot be objectively determined. PR can vary across individuals, situations and types of products and services (Day & Crask, 2000). In the online retailing context, the intangible nature of online transactions poses a risk for consumers, impeding further use of online purchasing services (Bhatnagar et al., 2000; Hansen, 2007). Previous research on its antecedent role also suggests that PR negatively impacts internet shopping (Liebermann & Stashevsky, 2002). By and large, perceived risk is conceptualized as a multi-dimensional construct in several information systems studies (Cox & Rich, 1964; Jacoby & Kaplan, 1972; Bettman, 1973; Bhatnagar et al., 2000, Forsythe & Shi, 2003; Zhang, Tan, Xu & Tan, 2012). This study adapted the perceived risk indicators from a review of relevant literature. These are i) fi nancial risk (Jacoby & Kaplan, © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 8 (4–27) 1972; Bettman, 1973, Bhatnagar et al., 2000; Forsythe & Shi, 2003), ii) performance risk (Jacoby & Kaplan, 1972; Bettman, 1973; Forsythe & Shi, 2003) and iii) personal/privacy risks drawn from work by Jarvenpaa & Todd, 1997; Tan, 1999; Forsythe et al., 2006. 2.2.3. Perceived Value Perceived value (PV) is a broad and abstract concept that refers to the benefi ts ascribed to the purchase/use of a product or service. As Monroe (1990) notes, value is “the trade-off between the quality or benefi ts [consumers] perceive in a product relative to the sacrifi ce they perceive by paying the price”. Customer value is usually operationalized as a trade-off between quality (benefi t) and cost (price) (Bolton & Drew, 1991). Consumers sometimes attribute value to an item because its consumption or usage serves as a means to an end (Day & Crask, 2000), or “value-in-use” (Woodruff & Gardial, 1996). In knowing how to manipulate perceived value, the marketing manager in turn has knowledge essential to satisfying customers (Day & Crask, 2000). The perceived value construct is multi-dimensional in nature (Sheth, Newman & Gross, 1991; Sánchez-Fernández & Iniesta-Bonillo, 2007). In this study, it has four dimensions drawn from relevant literature, namely i) monetary value, ii) convenience value, iii) social value and iv) emotional value. Online customer value can be different from its offl ine counterpart. In online retailing settings, not only the product itself, but also the web store and the internet channel contribute value to customers (Yunjie & Shun, 2004). Previous research established that perceived customer value is a signifi cant determinant of online transaction behaviour (Chew, Shingi & Ahmad, 2006). 3. THEORETICAL REVIEW This study is underpinned by four theories commonly used in services marketing and consumer technology adoption research. These are (i) Technology Acceptance Model, (ii) Perceived Risk Theory (iii) Theory of Consumption Values and (iv) Expectations-Artifact Model of Satisfaction. 4.1. The Technology Acceptance Model The Technology Acceptance Model (TAM) of Davis (1989) and Davis, Bagozzi and Warshaw (1989) is one of the most widely used models of information systems (IS) for explaining or predicting the motivational factors in user acceptance of technology. The TAM states that users’ positive perception of usefulness as well as perceived ease of use toward any technology will lead to a positive attitude toward using that particular technology, which in turn leads to the actual system use. In this study, the perceived usefulness (PU) and perceived ease of use (PEOU) indicators are drawn from the TAM. In spite of its effi cacy, several researchers have sought to extend the TAM by adding different constructs. For instance, a study by Lin and Sun (2009) that investigated the link between TAM factors and e-satisfaction in the online shopping context found a positive and signifi cant relationship between TAM factors and e-satisfaction. Alternatively, other studies have used the TAM in combination with other frameworks/models in various contexts to test its ability to predict different outcomes. For example, a study by Cho (upcoming) which focuses on consumer satisfaction in online shopping attempts to combine the expectations disconfi rmation theory (EDT) with the TAM. Similarly, rather than predicting the acceptance and use of IS, this study investigated how TAM factors amalgamated with other consumer behaviour frameworks might contribute to online satisfaction. © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 9 (4–27) 4.2. The Perceived Risk Theory The Perceived Risk Theory was fi rst introduced by Bauer (1960) in studying consumer behaviour. According to this theory, consumers perceive risk because they face uncertainty and potentially undesirable consequences as a result of purchase or usage of products/services. This means that the more risk consumers perceive, the less likely they will purchase/use a product or service (Bhatnagar, Misra & Rao, 2000). The perceived risk construct in this study is derived from the perceived risk theory and adapted to the online retailing context. The core constructs of the theory have been decomposed by researchers into several perceived risk dimensions. For instance, Cunningham (1967) conceptualized six dimensions of perceived risk: performance, fi nancial, opportunity/time, safety, social, and psychological risk, while Bhatnagar et al. (2000) argued that two types of risk exist when buying over the internet: product risk and fi nancial risk. These risks are thought to be present in every choice situation but in varying degrees, depending upon the particular nature of the decision (Taylor, 1974). Moreover, different individuals have different levels of risk tolerance or aversion (Bhatnagar et al., 2000). Findings from a study by Forsythe and Shi (2003) which examined the relationship between types of risk perceived by internet shoppers and their online patronage behaviours suggested that perceived risk is a useful context to explain barriers to online shopping. 4.3. Theory of Consumption Values The theory of consumption values (TCV) is a consumer behaviour theory that was developed by Sheth, Newman and Gross (1991a; 1991b). Over the years, TCV has evolved into a popular marketing theory and has been widely applied in various contexts, including IS. The theory focuses on explaining why consumers choose to use or not to use a specifi c product or service, arguing that consumer decisions are made based on perceived value. The TCV has fi ve core constructs which are conceptualized as fi ve different types of values (Functional value, Social value, Epistemic value, and Emotional value, and Conditional value) that underlie consumer choice behaviour. In this study, the perceived value construct is drawn from the TCV by Sheth et al. (1991a; 1991b) and adapted to the online retailing context. Kalafatis, Ledden and Mathioudakis (n.d.) postulate that all or any of the consumption values can infl uence a decision and can contribute additively and incrementally to choice; consumers weight the values differently in specifi c buying situations, and are usually willing to trade-off one value in order to obtain more of another. TCV’s strong point is its analytical strength, which helps practitioners to understand consumer decision making. This enables them to develop practical strategies that address real market conditions (Gimpel, 2011). 4.4. The Expectations-Artifact Model of Satisfaction The Expectations-Artifact Model of Satisfaction (EAMS) is a relatively new alternative framework that was fi rst proposed by Johnson, Der and Fornell (1996) as a response to the shortcomings of extant satisfaction models such as the Disconfi rmation Model by Oliver (1980) as well as the performance model. It posits that the primary determinant of customer satisfaction should be perceived performance. According to the model, expectations should have no positive or negative effect on satisfaction because they serve as neither an anchor, as in the performance model, nor a standard of comparison, as in the disconfi rmation model, for evaluating satisfaction. At the same time, perceived performance should co-vary with customers’ stated expectations. Performance gives rise to the expectations that customers report. Accordingly, the model posits a direct positive effect of perceived performance on satisfaction and a positive relationship between performance and expectations, without linking expectations directly to satisfaction, to capture these predictions (Johnson et al., 1996). © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 10 (4–27) 5. CONCEPTUAL FRAMEWORK The conceptual framework for this study is made up of various service marketing, consumer decision-making and IS constructs; it is based on the premise that customers’ perceptions have a direct effect on satisfaction with online retailing services. This study attempts to develop a comprehensive model linking the factors drawn from the TAM model, the PRT, and the TCV to customers’ satisfaction with online retailing services, which is drawn from the EAMS. However, the study does not specify how the different perceptual factors infl uence online satisfaction; instead it aggregates all factors into a composite variable – “customers’ perceptions” – and posits the nature of relationships between the independent variables and the dependent variable. The conceptual framework, a graphical representation of how these theoretical constructs are interconnected, is depicted in Figure 1. Figure 1 Conceptual Model Perceived Attributes – Usefulness – Compatibility – Ease of Use Perceived Risk – Financial risk – Performance risk – Personal (Privacy) risk Perceived Value – Monetary value – Convenience value – Social value – Emotional value INDEPENDENT VARIABLE CUSTOMER PERCEPTIONS DEPENDENT VARIABLE H1 – Level of Satisfaction SATISFACTION WITH ONLINE RETAILING SERVICES Source: Researcher, 2017. 6. METHODOLOGY 6.1. Research Design This research adopted a cross-sectional, descriptive, correlational study design that sought to establish the antecedent role of customers’ perceptions with regard to satisfaction with online retailing services in Kenya. Descriptive, correlational studies seek to investigate the studied phenomena but are not able to control or manipulate variables, and thus require the researcher to collect data and determine relationships without inferring causality (Swanson & Holton, 2005). © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 11 (4–27) 6.2. Empirical Model The antecedent role of customers’ perceptions in satisfaction was ascertained using a linear regression equation in line with Spencer et al. (2005). Since the customers’ perception variable was computed as a continuous composite/aggregate value made up of three constructs (perceived attributes, perceived risk and perceived value), the study equation was in the form of a simple linear regression, a data analysis technique for identifying underlying correlations among data in research (Nimon, 2010). The simple linear regression (equation 1) is illustrated below: Y = β0 + β1 P1 + ε1 (1) Where: Y = Customer Satisfaction (Dependent variable) β0 = Constant β1 = Linear regression coeffi cient P1 = Customers’ Perceptions (Composite Value) ε1 = Error Term 6.3. Sampling and Data Collection 6.3.1. Sampling The study respondents were 18,147 registered users drawn from six online retailing fi rms. A sample of 391 respondents was selected using multi-stage sampling methods including purposive, stratifi ed and simple random sampling. Purposive sampling was employed to select the 6 online retailing fi rms that were accessible to the researcher. Thereafter, stratifi ed random sampling, a probability sampling technique, was used to select the sample from the 18,147 respondents who were registered users of the online retailing services. Stratifi ed random sampling was employed because the sampling frame was not homogeneous since the sample contained sub-groups, thereby necessitating a fair representation of these sub-groups in the sample size (Ahuja, 2005). This technique ensures that observations from all relevant strata are included in the sample (Lemm, 2010). Stratifi ed sampling also guarantees that every possible sample matches the population distribution on strata-defi ning characteristics (Mallet, 2006). To this end, proportional stratifi cation technique was initially employed to arrange the study elements according to the respective strata. In proportional stratifi ed sampling, the population is divided into groups or strata. Samples are then selected (e.g., using simple random sampling) by strata, in proportion to strata sizes (Mallet, 2006). The six online retailing fi rms in Nairobi, Kenya formed the six strata from which the respondents were drawn. Subsequently, a sample was randomly drawn from each strata and categories using random sampling method. In simple random sampling, every possible combination of population elements is equally likely to be selected (Mallet, 2006), thereby eliminating possible bias. For this study, a computerized random number generator was used to select the respondents out of the whole population. This was aimed at eliminating bias in the sample selection. 6.3.2. Questionnaire Construction Primary data was collected using a 54-item questionnaire composed of three different sections (A, B & C) based on different scales of measurement. Section A consisted of 3 questions on demographic factors, i.e. age, education level and income level; Section B had 46 close-ended Likert scale type questions with seven-point rating scale ranging from 1 = “strongly disagree” to 7 = “strongly agree”; Section C consisted of 5 close-ended questions with 5-point rating scale © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 18 (4–27) If the R-Square value is 1, then there is a perfect fi t, whereas R-Square value 0 indicates that there is no relationship between the IV & DV. According to Table 4, the adjusted R-Square value = 0.531. This therefore means that there is a moderate relationship since the customers’ perceptions variable accounts for 53.1% of the variation in the satisfaction variable, implying that other factors affecting satisfaction that were not studied in this research add up to 46.9.1%. The adjusted R2 is important as it helps to discourage overfi tting of the model (Doane & Seward, 2011). 7.2.3. ANOVA Results Table 7 reveals the SPSS output for the analysis of variance (ANOVA). The ANOVA table tells us whether or not the model can predict Y using X. It contains the output for determining the statistical signifi cance of the model. Table 7 ANOVA Model Sum of Squares df F Sig. 1Regression 149.726 1 269.024 0.000 Residual 132.460 238 Total 282.186 239 a. Predictors: Customer Perceptions b. Dependent Variable: Satisfaction Source: Research Data (2017). The statistical signifi cance of the model was assessed using the following hypotheses: H0: β1 = 0; i.e. Variation in Y is not explained by variation in X H1: β1 ≠ 0; i.e. Variation in Y is explained by variation in X According to the H0, if a coeffi cient (βi) = 0, then the distribution of the response variable (Y) does not directly depend on the input variable Xi, which can therefore be “dropped” from the model. Therefore, H0 – the null hypothesis – implies that the model has no predictive value and H1 – the alternate hypothesis – implies that the model has predictive value. Since the signifi cance value (0.000) is less than 0.05, the null hypothesis that the model is not useful was rejected, implying that the model is statistically signifi cant and is therefore useful in predicting the relationship between customers’ perceptions and satisfaction with online retailing services. 7.2.4. Test of Hypothesis After establishing that the model is useful, the researcher tested the relative signifi cance of the independent variable in predicting the dependent variable. Table 8 shows the linear regression coeffi cients with the output data. Table 8 Coeffi cient table Variable βStd. Error t = β/S.E P-Value Constant –2.193 .330 –6.642 .000 Customer Perceptions 1.258 .077 16.402 .000 a. Dependent Variable: Satisfaction Source: Survey data (2017). © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 19 (4–27) The hypothesis that was tested regarded the relationship between customers’ perceptions and customer satisfaction with online retailing services. H 0 : There’s no relationship between customers’ perceptions and customer satisfaction with online retailing services. As the study results in Table 8 show, the null hypothesis which proposes that customers’ perceptions have no statistically signifi cant effect on satisfaction with online retailing services was rejected since β = 1.258 and p-value = 0.000. Consequently, the null hypothesis was rejected since β ≠ 0 and p-value ˂ a, meaning that customer perceptions have a statistically signifi cant effect on customer satisfaction with online retailing services. This outcome lends support to the fi ndings of Bolton and Drew (1994), who empirically established that customer perceptions have a signifi cant positive relationship with customer satisfaction in the service context. This is in line with Sing (1991), who argued that customer satisfaction can be understood as a collection of multiple satisfactions with various objects that constitute the service system. 8. FINAL CONSIDERATIONS 8.1. Conclusions The most important conclusion that can be drawn from the fi ndings of this study is that customers’ perceptions have an antecedent role with regard to satisfaction. In other words, the study concludes that customers’ perceptions are associated with satisfaction with online retailing services. 8.2. Implications of the Study The empirical fi ndings of this study have implications for scholars, practitioners as well as policy makers. 82.1. Theoretical Implications of the Study This study makes an important theoretical contribution to the study of online consumer behaviour by proposing and outlining perceptual antecedents of electronic consumer satisfaction. Moreover, by empirically testing the association between the two widely-used IS and consumer behaviour constructs, the study demonstrates that the proposed study model can be used to explain a signifi cant amount of variance in customer satisfaction with e-retailing. The research also shows that customers’ satisfaction with e-retailers depends on their perceptions vis-à-vis the website, confi rming that online consumer behaviour is subject to individual perceptions. It therefore provides future scholars with a useful framework of how to incorporate both IS and consumer behaviour theories and constructs in their research projects. 8.2.2. Practical Implications of the Study With the rapid growth of online retailing, customer satisfaction has become a major concern for online retailing managers and practitioners since customers are less likely to search for alternative purchase options when the current website offers satisfaction. The study therefore recommends that online retailing decision makers should put more effort in enhancing the quality of their services as a way of ensuring their customers are satisfi ed with their services. It is therefore imperative that online retailing fi rms have a good understanding of their target customers, since © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 20 (4–27) this will not only help in enhancing the desired levels of customer satisfaction, but will also help to increase customer loyalty towards their services in the long term. 8.2.3. Policy Implications of the Study Due to its ever increasing popularity, online retailing has had a signifi cant impact on several sectors including entertainment, consumer electronics, food and media goods, posing substantial challenges to consumers, industry players and regulators alike in the process. There is therefore a need for a regulatory framework that keeps online users safe while protecting the interest of all stakeholders, including the government. If need be, a quasi-independent multi-sectorial entity could be tasked with overseeing such a programme as is the case in other countries. 8.3. Suggestions for Further Study Since the model conceptualizes customers’ perceptions as a composite variable made up of perceived attributes, perceived risk and perceived value, it does not specify how the different perceptual factors infl uence online customer satisfaction; instead, it aggregates all factors into one construct called “customers’ perceptions”. Therefore, as a way of advancing and deepening our understanding of this link, future studies should consider investigating the specifi c nature of the relationships between perceived attributes, perceived risk and perceived value and electronic satisfaction. In addition, future studies might need to investigate other e-commerce sub-sectors/ context such as online travel reservation, e-entertainment as well as online education/e-learning amongst others for purposes of generalizing the research model. The current research is one of few studies in the online retailing context that have attempted to examine the relationship between what customers perceive and online satisfaction. However, in spite of the fact that this current empirical study confi rms that customer’ perceptions are an important factor behind users’ satisfaction in the online retailing context, it would be interesting to carry out qualitative, in-depth studies whereby online users detail their experience when shopping online. Such studies would give managers and practitioners deeper insight into what works and what does not insofar as online customer satisfaction is concerned, which will in turn contribute to reducing consumers’ reluctance to purchase online. 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Ohio: Thomson South-Western. © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 25 (4–27) APPENDIX APPENDIX 1: QUESTIONNAIRE INSTRUMENT SECTION A. CUSTOMER DEMOGRAPHIC FACTORS First things fi rst: Tell us a bit about yourself. Please respond to each item by choosing the response that best describes you. 1. 2. 3. Age:  18 – 23  24 – 29  30 – 35  36 – 41  42 – 47  48 years & above Highest Level of Education:  High School Certifi cate  Diploma  Bachelor’s Degree  Masters Degree  Doctorate  Professional  Other Monthly income (gross):  Below KSh 24,999 KSh 25,000-49,999  KSh 50,000-74,999  KSh 75,000-99,999  Ksh 100,000-KSh 124,999  Ksh 125,000 & above SECTION B. CUSTOMER PERCEPTION MEASURES Please indicate the extent to which you disagree or agree with each of the following statements by marking with a cross (X) in the appropriate block provided. Please use the following seven-point rating scale ranging from 1 = “strongly disagree” to 7 = “strongly agree”. CUSTOMER PERCEPTIONS Value Label Variable Label 1 2 34567 Perceived Attributes 1. The system enables me to accomplish what I want more quickly 2. The system makes me more effective 3. The system makes it easier to do what I want 4. I fi nd the system useful 5. The e-commerce service fi ts my image well. 6. Using the system is compatible with all aspects of my lifestyle. 7. I think that using the system fi ts well with the way I like to do things. 8. Using the system fi ts into my lifestyle. 9. I fi nd the system to be clear and understandable. 10. It’s easy to get the system to do what I want it to do 11. It’s easy to fi nd what is being sought 12. The system has no hassles © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 26 (4–27) CUSTOMER PERCEPTIONS Value Label Variable Label 1 2 34567 13. Learning to operate the system is easy for me. 14. Overall, I believe that the system is easy to use. Perceived Risk 15. This service costs more than conventional methods 16. I might be overcharged for using this service 17. I might not receive the product/service that I paid for 18. Inability to touch and feel the item worries me 19. One can’t examine the actual product 20. It’s not easy to get what I want 21. Information takes too long to come up/load 22. The e-commerce service failed to perform to my satisfaction 23. My credit card number may not be secure 24. My personal information may be sold to advertisers 25. My personal information may not be securely kept Perceived Value 26. This e-commerce service is reasonably priced. 27. This e-commerce service is competitively priced 28. This e-commerce service offers value-for-money 29. Using this e-commerce service is economical 30. I can use this e-commerce service anytime 31. I can use this e-commerce service anyplace 32. This e-commerce service is convenient for me to use 33. I feel that the e-commerce service is convenient for me 34. I value the convenience of using this e-commerce service 35. This service would help me feel acceptable by others 36. This service would improve the way I am perceived 37. Using this service would make a good impression on others 38. My friends and relatives think more highly of me for using this service. 39. This service would give its user social approval 40. I enjoy using the system. 41. Some aspects of the system make me want to use it 42. I feel relaxed about using the system 43. Using the system makes me feel good 44. Using the system gives me pleasure 45. Using the system is fun 46. It’s exciting to use the e-commerce service © Faculty of Management University of Warsaw. All rights reserved. DOI: 10.7172/2449-6634.jmcbem.2018.1.1 Journal of Marketing and Consumer Behaviour in Emerging Markets 1(7)2018 Peter Misiani Mwencha, Stephen Makau Muathe 27 (4–27) SECTION C. CUSTOMER SATISFACTION MEASURES Please indicate the extent to which you are satisfi ed with the e-commerce system by marking with a cross (X) on one of the fi ve blocks provided below the position which most closely refl ects your satisfaction with the service. 1. 2. 3. 4. 5. How satisfi ed were you with the online retailing service initially?  Very Dissatisfi ed  Slightly Dissatisfi ed  Neither  Somewhat Satisfi ed  Very Satisfi ed To what extent does this online retailer meet your needs?  Extremely well  Pleased  Satisfi ed  Mixed  Extremely poorly My experience with this online retailer was very satisfactory  Strong Yes  Yes  Neutral  No  Strong No Overall, I am _ with the service?  Delighted  Pleased  Satisfi ed  Mixed  Mostly Dissatisfi ed If I could do it all over again, I would still use this service?  Strong Yes  Yes  Neutral  No  Strong No Thank you very much for your time.